{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c5664e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.12.1\n",
      "IPython version      : 8.20.0\n",
      "\n",
      "Compiler    : MSC v.1937 64 bit (AMD64)\n",
      "OS          : Windows\n",
      "Release     : 10\n",
      "Machine     : AMD64\n",
      "Processor   : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel\n",
      "CPU cores   : 8\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: \n",
      "\n",
      "statsmodels: 0.14.1\n",
      "numpy      : 1.26.3\n",
      "matplotlib : 3.8.2\n",
      "pandas     : 2.1.4\n",
      "watermark  : 2.4.3\n",
      "seaborn    : 0.13.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "\n",
    "import watermark\n",
    "\n",
    "plt.style.use('personal.mplstyle')\n",
    "tab10 = plt.get_cmap(\"tab10\").colors\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext watermark\n",
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25c1a4b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"simpeldata.csv\", sep=\";\")\n",
    "\n",
    "data_alt = data.copy()\n",
    "data_alt[\"Treatment\"] = data.apply(lambda x: True if x[\"group\"] != \"Control\" else False, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74adc0e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "_GROUPS = data.group.unique()\n",
    "_TIMES = [\"PreFreq\", \"Post2Freq\", \"Post4Freq\"]\n",
    "_COLORS = {\n",
    "    \"Control\": tab10[0], \n",
    "    \"Treated\": tab10[9],\n",
    "    \"Reject\": tab10[1], \n",
    "    \"Source\": tab10[2], \n",
    "    \"Debunking\": tab10[3]\n",
    "}\n",
    "_LABELS = {\n",
    "    \"Control\": r\"Control Group\", \n",
    "    \"Treated\": r\"Intervention Group\",\n",
    "    \"Reject\": r\"Intervention: Rejected by Gatekeeper\", \n",
    "    \"Source\": r\"Intervention: Source Exposure\", \n",
    "    \"Debunking\": r\"Intervention: Debunking\",\n",
    "    \"PreFreq\": r\"\\begin{center}$2$ weeks\\\\pre-treatment\\end{center}\",\n",
    "    \"Post2Freq\": r\"\\begin{center}$2$ weeks\\\\post-treatment\\end{center}\",\n",
    "    \"Post4Freq\": r\"\\begin{center}$2-4$ weeks\\\\post-treatment\\end{center}\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9daaee20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>freq</th>\n",
       "      <th>group</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>PreFreq</td>\n",
       "      <td>1</td>\n",
       "      <td>Control</td>\n",
       "      <td>(Control, PreFreq)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Post2Freq</td>\n",
       "      <td>1</td>\n",
       "      <td>Control</td>\n",
       "      <td>(Control, Post2Freq)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Post4Freq</td>\n",
       "      <td>0</td>\n",
       "      <td>Control</td>\n",
       "      <td>(Control, Post4Freq)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>PreFreq</td>\n",
       "      <td>9</td>\n",
       "      <td>Control</td>\n",
       "      <td>(Control, PreFreq)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Post2Freq</td>\n",
       "      <td>15</td>\n",
       "      <td>Control</td>\n",
       "      <td>(Control, Post2Freq)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   level_0    level_1  freq    group                 label\n",
       "0        0    PreFreq     1  Control    (Control, PreFreq)\n",
       "1        0  Post2Freq     1  Control  (Control, Post2Freq)\n",
       "2        0  Post4Freq     0  Control  (Control, Post4Freq)\n",
       "3        1    PreFreq     9  Control    (Control, PreFreq)\n",
       "4        1  Post2Freq    15  Control  (Control, Post2Freq)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_stack = data[[\"PreFreq\", \"Post2Freq\", \"Post4Freq\"]].copy()\n",
    "stacked_data = to_stack.stack().reset_index()\n",
    "stacked_data[\"group\"] = stacked_data.apply(lambda x: data.loc[x[\"level_0\"], \"group\"], axis=1)\n",
    "stacked_data.columns = [\"level_0\", \"level_1\", \"freq\", \"group\"]\n",
    "stacked_data[\"label\"] = stacked_data.apply(lambda x: (x[\"group\"], x[\"level_1\"]), axis=1)\n",
    "stacked_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fd8cd68",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 4800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 4))\n",
    "gs = fig.add_gridspec(1, 8)\n",
    "\n",
    "_TREAT = {\"Control\": stacked_data.group == \"Control\", \"Treated\": stacked_data.group != \"Control\"}\n",
    "locs = [fig.add_subplot(gs[:, :4]), fig.add_subplot(gs[:, 4:])]\n",
    "\n",
    "for i, g in enumerate(_TREAT):\n",
    "    ax = locs[i]\n",
    "    sns.swarmplot(ax=ax, data=stacked_data[_TREAT[g]], x=\"level_1\", y=\"freq\", color=_COLORS[g])\n",
    "    \n",
    "    avg = stacked_data[_TREAT[g]].groupby(\"level_1\")[\"freq\"].mean()\n",
    "    for j, t in enumerate(_TIMES):\n",
    "        ax.eventplot([avg[t]], orientation='vertical', lineoffsets=j, linelengths=0.5, color=\"k\", ls='--', lw=3)\n",
    "    \n",
    "    ax.set_title(r\"{} ($n={}$)\".format(_LABELS[g], stacked_data[_TREAT[g] & (stacked_data[\"level_1\"] == \"PreFreq\")].shape[0]), fontsize=26, y=1.01)\n",
    "    \n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.xaxis.set_minor_locator(mticker.NullLocator())\n",
    "    ax.set_xticks([j for j,t in enumerate(_TIMES)])\n",
    "    ax.set_xticklabels([_LABELS[t] for t in _TIMES])\n",
    "    \n",
    "    ax.set_ylim(bottom=0, top=30)\n",
    "    ax.yaxis.set_major_locator(mticker.MultipleLocator(5))\n",
    "    ax.yaxis.set_minor_locator(mticker.MultipleLocator())\n",
    "    ax.set_ylabel(\"Consumption Rate\")\n",
    "    \n",
    "    if i == 1:\n",
    "        ax.yaxis.tick_right()\n",
    "        ax.yaxis.set_label_position(\"right\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"Figure1.png\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d69a6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZFqlWq3MH4EejUfT7/b35rAAAAHBfrVZrIxPNYdu9fv164euVSmXjY4YAAHAIxNLA7jk/P492u33ruUqlkktSlW63u3BBtvF4XNicCnEoANtLkgYAAO6o1+tLvW8XVzUF2CbLJsRptVo6UIHCvD8w9b5l7w2B4u3DxIV9+AxZarXawraz2Wy6twMAAABgpvdX+3zfs2fP1lQSAAA4XGJpYPcMh8NoNBq3nkuSJDqdTi7b/+qrrxa+/u7du8KSNIhDAdhekjQAAHBLt9udm2nxpmazKRMsazMej+Pdu3dzX7dSCLvo/Pw8RqNR5vtqtVouWXwB5lk0YTpJEgM4APdQqVTmBtT3+/1CV1AAAID36WMHbtImwHbLGj/UZw8AAMUTSwO7Z9aiaa9evcpt+8fHxwtff/z4cW77mkUcCsB2kqQBAIBbnj9/nvmeUqkUZ2dnaygNfO/58+cLVwyZTCZrLA083Hg8vpOxd5Y0TQ9itWlgc0ajUQyHw7mvf/7552ssDcD+ePbs2cLfMO122+9qAADWRh87cJM2AbZXVoKGJElMuAAAgIKJpYHd02g07vymLpfLuS5IuSg5S6lUKvz3ujgUgO30waYLAADA9jg/P4/xeJz5vjyzSgIcopcvXy71PgkagKJltTP1en1NJQHYL5VKZeEA/OvXr9dXGAAAAAB2QlaShqJX5QQAAMTSwK4ZjUZxfn5+5/llFlJbxbxF15IkiU6nk+u+ZhGHArCdJGkAAODaMpOGK5VKlEqlNZQGYD+Nx+OZHcLvK5fLCzPvAuRhUXbtJEnc9wE8wKJ7ueFwmBl0DwAAAMBhWWZRDQAAoFhiaWC3zEqcUiqVCom/rdVqcXl5GWdnZ1Gr1aLZbMbXX38daZrmvq9ZxKEAbB9JGgAAiIiIdru91ID/q1evii8MwB5bJiFORHZGboCHGo1GCwdmJIoBeJjT09OFr/f7/TWVBAAAAIB98O7du00XAQAA9ppYGtgtw+FwZuzFixcvCttnmqbRbDaj1WrF2dlZJElS2L7eJw4FYPtI0gAAQERENJvNpd6zzo4EgH3Ubrcz31Or1daWWRc4XIsy/0dkD+oAsFhWgE6n01lTSQAAAADYBVnxGOPxeKnFNwAAgPsRSwO7pdFozHy+UqmsuSTrIQ4FYPtI0gAAQPT7/YWZX6dqtdoaSgOwv7rd7lKBU/M6jgHy9Pr164Wvy/4P8DBpmi4MrO/3+4LqAQAAALj2+PHjzPe8efNmDSUBAIDDJJYGdsdoNIp+v3/n+X1N0BAhDgVgG0nSAABAtFqtzPeUy+XMVRsAWGyZ9rZUKkWapmsoDXDohsPh3NeSJNEWAeQgK0jn4uJiTSUBAAAAYNuVSqXM9ywz3ggAANyPWBrYHc1mc+bzz549W3NJ1kscCsB2kaQBAIDodruZ76nX62soCcD+Go/HM7P2vu/FixdrKA1w6LLaI5n/AfJxenq68PVer7emkgAAAACwC7ISNQyHw6XGHAEAgNWIpYHd8ubNm5nP7/u5Kg4FYLtI0gAAcOCWGbxPkiQqlcoaSgOwv5YNltLeAuuQNRiTNZgDwHKyBv8F1AMAAABw0zKTSSyyAQAA+RNLA7uj3+/HeDy+83yappEkydrLs07iUAC2iyQNAAAHrtPpZL7n888/X0NJAPbb69evM99Tq9XWUBKA71faWmTfM4oDrEtWAEBWewwAAADAYXn27Fnme0ajkUQNAACQM7E0sDtardbM5w/hPBWHArBdJGkAADhwb968yXxPtVpdQ0kA9tsy2Wm1t8C6ZLVJaZquqSQA++/JkycLX7eKAQAAAABTpVIpSqVS5vva7bZEDQAAkCOxNLA75p2vJycnay7JZohDAdgeP9h0AQAA2JzxeBzj8TjzfVk/5A/JeDyON2/exGAwiIuLixiNRtff4TQzZZqmkaZpPHv2bKngCVgHdXezbn7fi2hvt59ziX0wGo0Wvr7pQeXxeBwvX76Mfr9/fY6laRqlUimePXsWlUrlwdvv9/vx+vXr6+1Pv5Ob5/Dp6Wl8/vnnCzOPAywjq10dDocHsZoDAACs4mY/3Gg0un5ExPVv9yRJ4vT0NMrl8sb7M8iPY8+m6P9nmzSbzTg9Pc18X7vdjoj5K4juC+cny+r3+9Hr9WI4HMZoNIp3797dGqdPkiQeP358XV9OTk42dj/hngd23y5en0ajUfT7/VttT1ZbeXx8HOVyeSvKT35ch+4SSyOWht0xHA7nxuNu+lxdF3EoANtDkgYAgAO2TJbEaWfroet2u9FqtRZ+Z9MOyeFwGBER5+fnkSRJ1Gq1tQ20nJ+fz31tWpYs3W43er3encGjmx2t2zR4tG7tdnth594qnd03B76m33dExNXVVR5FjYjdqbv7bvrdLnJo7a1ziWVkJTdZ9pyZDiT2er3rIIP3AyTK5XJUq9WDGJzIapM2OVjVbrej0WjcOfbTgIButxtpmkar1Vr5WPX7/Wg0Ggs//3g8juFwGMPhMLrdbtTr9ajVatFoNA5mEA/IX9ZKDV999dWaSgIAQBZ97JvX7Xbj9evX0e12577n5sSB6ftKpVK8ePHiwQHps+xaX+au2sZjr004DLvS/68+HpbpRLisiWIR31+n3rx5E69evSqkLdykXTk/982u3fsMh8NotVrx5s2bzLHF6WI2759bSZLE559/HvV6vfA6s433PHAfhzyWv2vXp+nE7m63u9S9xby2MiKuj4UJ2rft0rXTdWg+sTRiadgdi67Bh7JQmjgUgC0yAQDgYJ2dnU0iYuGjUqlsupgb1Wq1JkmSZH5PyzzK5fLk8vKy0PIu2n+apnP/7vLycnJ2drbyZ03TdNJsNh9U5svLy1y+3/s87mPRd1QulzP//urqanJ2djZJ0zTXcr1vn+pukiSF7nsdarWa9vY9zqViZJUhz/1fXV1l7u/q6upB+8j67hcZDAaTWq22sI7MeqRpOul0Og8q97bLugc8OzvbSLlardat49BqtSaDwWDS6XQm5XL5TjlbrdZS2+31enfqQZIkk1qtNmm1WpNerzcZDAaTwWAwabVak1KpNPN72fd6wf7qdDo7f++x659hMBhkXnsAAJhvmb61ZX8jZrnvfZs+9ocbDAYr9+PMepRKpclgMMi1bOvuy7zZRzLvkfdnnCerHymP3zPbfOy1CcW3CYu2V/T40D71/29rfczS6/Uyy9Hr9QotwzZb5vuZd9weOj6zaft0fkbs1/jcNo3j5nUP8f6jVCoV0vZs8z3PNlkmlm1TY5n3cXl5uVR7VvRnurq6Wqocq4yHHuJY/q5dn66urpbqU7nvo1Kp3Ls9ytq2a+dq59QyXIeyiaURS8PuqFQqhbWXu0IcCsD2OJyrDwAAd8zqoLtvh92+GQwGczsVH/oosrN21Q6Xq6urhZ1Vyz7SNL334MiuBYvdd1BllYGvh9jHursPSRqWOSaH1t46l4qRte99Gsied4wXDQyu8lhmoHxXZd0DbmIA9Waw6bxJ17PKnVXW99uLWq22VMDAvHbG4DK7aNcTHEwmu/8ZlrlmAgAw3zYnadDH/nBFTRrJsw9u3X2Zy/yGWNfEgKz+uYdM4N6FY69NKL5NWLS9osaH9rH/f1vrYxZJGrI9pJ0slUqTZrO5U5Po9vH8jNiv8bltGMedTB52bqxyDuVx/uzCPc822bckDZNJ9gS66aPIcchl2tZVv9dDGsvfxevTYDDILaFE1qNWq61cvqxtunYud04tw3VoeWJpxNKwO+YlnVk2MUGn07lOGDVtw5MkmZRKpUmlUpmcnZ1t/W9pcSgA20OLCwBwwJbJjLvtnQxFaDabhQ9OFBXMkrXPm3q9Xu6DMfcJktm1YLH7DKqs+l3f177W3X1I0rDM8T+09ta5tP5zKWK/BrLfP8Z5rlpR9HHatKx7wE20R9MyLQpQmDVJO0mSmfXs6urqVqDMfVYimRfAvI91gv226wkOJpP9+AxZ1yhtCwDAfNuapEEf+8NDjpZdVfa+j7x+M2yiLzNrYvU6+s2XmTz9kEQCu3DsF+1Dm5BPm7Duer6v/f/bWh+zSNKwnDwmhSZJMqlUKpNWq/XgsZui7Ov5GbFf43ObHscton3Lejxk0uGu3PNsk31M0jCZ3F4FfV3txdQyv6nvk/TgUMbyd/H6tMw9Vp6P+yTvW+e5cMjXTteh1YilWY5YGjZtUbuelRhn1r1mmqaTcrk8KZVKd9rMNE0nZ2dnW1u/xaEAbIcPAgCAgzUajTLfk6bpGkqyPer1ejQajcL3MxqN4uTkJIbDYeH7muX8/DxOT09jPB7nut1qtbpUvTok9Xq9kO961n4Ooe7uqmWO/6G1t6tyLjHP9Lus1+u514/ptouud+uWda0ulUprKsn3zs/Pr8vUarXmvq9cLt95bjwex8uXL289NxqN4pNPPrk+v1qtVvR6vZXb2WazOfP5er2+0nYAIrLv9fyOAgDYLfrYH24d/S7dbjeq1Wph258nj77MZ8+eLXx9PB5Hv9+/9/aX0el0Fr5eLpfv1a+9j8dem7AbDqX/X33cP2/fvo0kSR60jfF4HN1uN+r1ejx69CiOj4+j0WhszTjVoZyf+2pd47j9fn8t+8nLPt7zcH+1Wi3Ozs4y33d6eprrfrvdbrTb7YXvSdM0er1ervud2vWx/F28Po1Go9zrUZZ1xxfsg3VcO12HVieWZjliadi0RefqvN/O/X4/Pvnkkzg/P480TaPVasXl5WVMJpO4vLyMXq8Xg8Egrq6u4vLyMprNZiRJEqPRKM7Pz+P4+DjOz88L+kT3Jw4FYDtI0gAAcKCW7Xx96GD/LqlWq5kDUzclSRLlcjkqlUrUarUol8srdcSOx+M4OTkpPIjufY1Go7ABpPF4vFcd7w8xHo/j+Ph4pTp1X4dSd3eV9vZhnEss0mg04vj4uNBguvF4HE+fPi1s++u2jYFr04Hhs7OzhYNH89rJm+ft+4EGg8EgarXavcqVpunMc7rf7wvgBFZmcBwAYH/oY3+44XC40kSBWf1wy/andrvdtQXQ5tmXWalUMj/jogD9PGR9jvvU1X089tqE3XAo/f/q435KkiQGg0GuY4nTiSYnJyfx6NGjqNfrG+v3PpTzcx+tcxx3mqDhPtI0jTRN730O3WdC5j7e82yT8/PzODo62ujj0aNHK5e72WzOnEh7U54T7EejUea1O0mSwhI07PpY/q5en1a5XyuVSnF2dhbNZjOazWbUarWoVCorl/3Jkyf3KepBWte103VodWJplieWhk27uLiY+9rjx4/vPNdut6/vrzqdTlxeXkatVpt7XqVpGmdnZ3F1dXXrPFnHvc2qxKEAbIkJAAAHaTAYTCIi83EoKpXKUt9HkiSTZrM5uby8XLi9Tqez9DYjYtLr9XL5HIv2kabppNlsLnxPqVSaVCqVSa1Wm5TL5UmpVJokSbL057jP57m8vFx5+3k97mPR91EulyeTyWRydXU1SdN0LeU6hLqbJEku+9gU7e1szqX1n0sRkVnuVVxdXWXu7+rq6kH7uM81aNYxS9P0wds6OzvL54vbsKw2KU3TtZan0+msVF/mlXswGEwmk8mtNmP63EPUarW9rg8chpvn2c1HpVLZdNGWtg+fYV57ol0BAMiWdS8VEZNWq5XLvrJ+M+tjv/24j6urq6W/k0qlsvC7GAwGS/fBPaSfYFN9mcvU/aLM+x32kH3v4rHXJhTfJizaXl7jQ4fQ/7+t9TFLr9db6/523eXl5YOuNcs8SqXSWr/zQzg/I/ZrfG4T47jLtBU3H7VabTIYDBZ+D5eXl5NOpzOp1WqZn2NVu3jPs03Ozs4KbefyejzkPmWZc6fZbD74u1xmPw9pB/d5LH9Xr0/L/I5b9Vo/GAwmzWZzUi6Xcz0Xssrp2nn3scrndR1anVia1YilYZMW9X+8Xwdbrdb1OXzftn66jZuPTqeTwyd5OHEoANvhBwEAAHMcyqru7XY7ut3uwvckSXKdMXoZlUolKpVKjEajqNfrmVmuq9VqfP3114V+56PRaObKKWmaRqPRWPjZ+v1+dDqdpbM4NxqNGAwGS703TdPMrOzVanVhtuKisrrf19OnT5fOQFoqlaJcLsdnn30WSZJkZja96VDq7iFY5bgfEucS91Wr1aJarc5ciWQ4HMbr16+j3W6vlAn//Pw8Tk9PM1c32XaLsolHrP/+b5r5f5nVIRcdr+l9zrTN6PV691rZ6H3Hx8czn7fCFrCqee3JlBUMAAC2nz72fGSVJeL7z9TpdDJ/25dKpeh0OjEcDuPp06cLt/v8+fOlv9P7KKIvs16vZ9aZbrcblUpl5fJmabVaC1+/zz737dhrE3bDofT/b2t9JF9pmsbl5WVUq9XMen1fw+EwTk9Po1KpRLPZLHQM81DOz321rnHc8Xh8veptllartXRdSdM00jS9vqcZDofx8uXLO3XyPmNN+3bPQ/4Gg0F88sknC49no9G4Pnfuo1qtZp6jzWZzI2Pf2z6Wv8vXp6zfcbVaLfM97yuVSlEqleLs7CzG43G8efMmms3mdf168uTJSts7ZOu6droO3Y9YmtWIpWGTLi8v57724x//+Pr/h8Nh1Ov1KJVKD2qfptf7er1+/Vy1Wl3p90dRxKEAbIlNZ4kAAGAzlsk0v+7sr5uwzAr3aZo+ODtz1solcSNr8kNk7eOh+xwMBktndH5ohumb8l654CGyMl8vkz06TdNJq9V60Hd0SHU3r5WSNmWZ9rZUKm26mGvnXNrMdWCfVhuY9Tg7O1tpn8scl307V7M+8zpXpb+5qt8yGcYXrQJ4814hz2zls7KhTx+wK+atmrPO8/2h9uEzLGpP9uUaAwBQlKzVoCJi0mq1ctnXKv0E9+nPOfQ+9qz74ul3ep/PvsyKlPftM9hkX2bWZ8qjT/F9y/T7rbqy664ee21C8RaV46HjQ4fW/7+L9XGZMbSHrPC9zwaDwaRUKq1cL1Z95LGS+7zyH9L5uU/jc+scx51MJnNXbr/5KJVKuX3Hl5eXtz7fqn3Qu3rPs03Ozs4Kb9vyeKzjPiVJknvV7WXqYR7jK/s4lr/L16es60Pe41C9Xm+Spum9V+jO+vyunffjOnR/YmlWI5aGTVr0G+Hmb9g0TSdJkuTWhzFrnCKvcYn7EocCsB3cAQEAHKh5k1wO7cf5OoPblgkyeWiQQ9b28+g4vry8XGqgLc/Op20KFlt1kPHmI0mS3L6XQ6q7u56kQXs7m3NpM9eBfRrIvvl4SCDEKoGnEbsfEJoV2FSr1dZWlpuD3MtYpj3Nu/z7OrDc6XQmpVLpQW3xLj+SJJmkabrW+r5J+5DgYB8+Q9Z9wCEkSQQAuK9tTdKgj301iwLGp4+H9pNm7eO+299kX+Yyk3fynLw/mWQH967aZ7/Lx16bULw869r7Dq3/fxfr42AwmNRqtYWPwWCQy7721bSv977XqSLr0yKHdn7u6/jcvLY7rzZi2YmmRZhOPl6lbuzyPc82OZQkDZPJcmOfq44dLJNkIK96so9j+bt8fcqqT0XFOdz3mpP12V07V+c69DBiaVYjlmY/H7sSS7PoN/D02jk9p/O+/s2qG5vstxCHArAddvsOCACAe7Oye3bHap4ZNKeWGUR+yD6X7Ux7aMfLMh27eQZrbFOw2H07YPP8Pg6t7u56koZl2ttD7Ax1Lm3mOrCPA9l5BEIsk/F/+tj1+6OsFRruu9LFfUwHzZY9hlnnbBFt6aKJEHm3D+t0qAPKsx6bzmq/DvuQ4GAfPsMygZEAAMy2jUka9LGvbplVI/Pou8r6bu+zj032ZS7TB5f3b9usyb6r9h/t8rHXJhRvUTkeMj50iP3/u1ofycfl5eXk7OyssL7fPCelHuL5uY/jc0W3Dct8znWMsa/yXe7yPc82OaQkDct+3mXPrWXGvPNsY/dtLH/Xr09Z29o262gvpw7l2uk69DBiaVYjlmb/H9scS7Oo77LVal0nlCmi72LW9XaTsb/iUAC2ww8CAADmGI/Hmy5CYcbjcZyfny98z9u3byNJklz3W6vVotfrRbfbnfueRqMRrVYr1/2+76Hbr1QqkaZpjEajue9Z9NqhaTabcXZ2lsu2Dr3u7qt3795tugg7wbnE+0qlUvR6vQdvJ0mSGAwG8cknn2Te/wyHwxgOh1EqlR68303I+nzHx8drK8dwOIyIiGq1utTfTN8/T6fTeXC53nd5eTn3tbzbBzbj8ePHmy4CByJN000XAQCAHOljX814PF7YHxbx/e/6PO6bs77bVqsVzWbzwfvJkldfZpIkUS6Xo9/vz31Pq9WKWq324H1FfF/vsvpg6vX60ts7lGOvTdguh97/rz4epjRNo9lsRrPZjOFwGP1+P3q93sLrxyr6/X6cnp4+eEzm0M/PfZbnOG5ExMuXLzPfk8cYYZZl6+Kh3PNsi1KpFE+ePNloGfJqp5rNZoxGo4X1p9vtxvn5eeY5Vq1WM6/RRbSx8+zSWP4+XJ+Ma++evGOgXIceRizNarQ5+2+bY2myztdpG1REW1Sr1aLRaNwqw2g0inq9vpHfk+JQALaDJA0AAAdqmQ6UfZ40nDWge3Z2Vtjky1evXi3sFG+329FsNgvrrEzTNMrl8oO3U6lUFg5QCc753rqDEfa57u6qZdrbfU6KkxfnErPkOZiSJEm8ffs2Tk5OMt/7+vXrnU3SkHV/t65BtpuBocvelywKJq3VaoUck4uLi9y3uQ3evn0brVYrRqPRXt/zL5KmaXz22WdRqVQ2XRQOxDLX9fF47PoPALAD9LGvrtFoLHy9Vqvl+vus2WzODWTPa7Jq1v7z7Mus1+sLyz0cDmM0GuUSlJsVTFwqlVbazyEce23C9jnk/n/1kYjv2+pSqXR9LZombOh2uw86dv1+PxqNxoPGZg75/Nxned/7RHx/PLL2uU0Tkg7hnmeblMvlnZwAPE+n04mTk5OFk2wbjUaUSqW51/nz8/PMY99qtdY6xr1LY/n7cH1adI0Xk7R98r52ug49nFia1Yil2V+7EEuz6NiMx+Not9vXCWWKUKvV7vSZtNvtaDQaa/+NIg4FYEtMAAA4SJeXl5OIyHzsqyRJFn7uq6urQvd/dna2cP9nZ2f32u4yx7TVauXyGTqdztrqT5qmW1NPs+rOzUelUln7/vex7iZJkvOnWK9Db2/ncS5t5jpweXmZ22e4urrK3N9Dv8es49Tr9fL5MDfUarXMz7XL7VLWNbXT6aylHJVKZRIRkzRNl3r/YDBYeDyKOmfn1cFSqVTI/qAI8+7bi7i+FmUfPsNkst7rNADAPlnmt3pe/d7L9FXpY1/NOvqQZlnUr5Tnttb1OyVrv81mM5f9ZNWVVer/Phx7bULxFpXjvv2wh9r/v6v1kfW6urqatFqtSblcXvra9v7jIeMIh3p+7tv43M1HEfc+rVarkOtDUfbhnmebZJ2nDzlXt9nV1VXmuTdvTHTROOr0UavVci/zPo3l78P1Keu72bYxKNfO/LgO5UMszWrE0rBJi9qf6bk8GAwK2/+8866I+61lrPOaCsBsHwQAAAdp2WyN+5hJud1uL/xctVqt8KyRL168WPj6opVJHurzzz/PZTsyay6Wpml0Op1ct3nodXdXHXJ7mwfnEuvWarUyj/94PF64ksk2y8r0vq7r+/T7Wzbz/6Is/C9evCik3OPxeG5bsU2rMwH7w/0gAMBu0Me+mqwVFIvqi1vU51DUqo5F9GVO1Wq1ha+3Wq0H72M4HGausL5K/T+UY69N2C6H3v+vPpIlSZKo1WrR6/ViMplEp9NZub/7+fPn9+rHOvTzcx8Vde/TbDYXvp51HNftUO55KFaSJPH27duF7xmPx/H06dPM595XKpVy+b2wDYoYy9+X61NWGbPaVtajiGun61A+xNIsTywN22w0GkWSJFEqlQrbR6lUmnluZd1TbMo2lglg30jSAABwwJbpgMvqfNxFWR3d9Xq98DIkSZLZkVrE5M95nUP38fjx48z3HHLnThEDnIdcd3fdMuddVvDrQ5yfn0e1Ws310Wg0CivvTc4lNiEr6D1iNweVt8m0zatWq0u9v9frzX1tmeN1H2/evJn72meffVbIPgEAANhu+thXt+g3fURxfXGnp6dzX8sq030VOfEp63sajUYP7k/MKn+lUlmp/h/CsdcmbJ9D7v9XH7mPSqUSl5eX0el0lq4/4/H4XuOEh3x+7qsi7n1Go1HmuHlR40L3dQj3PKxHqVTKbCuHw+GtOvX06dOF1+Rlkj/smrzH8vfl+pQ1ft1ut6Pb7a5cNvJVxLXTdWi/iKWBbFl9FssmOXmIJ0+ezHz+5cuXhe8bgO0jSQMAwAFbJmvpPg5AZw3EFJlB86ZFHdUREa9fv859n/M6hu7DCirzlUqlQjr6Drnu7rpl2tsiJ3x/9dVX0e12c32sY4K6c8m5tCnLrMDz1VdfraEk+csKnF0m+DYPnU4nzs7Olj7H5523aZoWdk+yaDB7HQN6wOHZxySJAAD7Rh/76hYFbhe5qtmi3+5FJMwtqi/z5vaz+pkfOtlj0bGKWH1ixyEce23C9jnk/n/1kYeoVCrx9ddfL32O3GeF0EM+P/fRJsdxt62NOoR7HtanUqlEs9lc+J52ux3tdjsajUZmXN/bt2+37px5qLzH8vfl+rRMm7zOBVm4q6hrp+tQPsTSLE8sDdvu2bNnhe9jXtu6jQmRxKEAFO8Hmy4AAACbk6Zp5mBNr9eLSqWyphIVL2tgZZ2dhFn7KmIC9MnJSW7bWlfH8y7KGjC9j0Ovu7vuyZMnme3trk74LpJzybm0KdOVKhYdg31MZLVOlUpl6XvM0Wg0d0C8yHN20fFfVzAOsF+SJLHKJADAjtPHvppFv+kjvg+Af/To0foK9P8rYrJAEX2Z76tUKnF+fj739Tdv3tw7UUO/389cgXeVfphDOfbahO1y6P3/6iMPlSRJDAaDOD09XaqOvnz5cunr36Gfn/uoqHufrJW31zHhahWHcs/Dep2dnV0vxDHPMgnUWq3WXo5p5jmWv0/XpyRJolKpZE4OPT8/j/Pz8zg7O4tnz57tZR3ZVkVcO12H9o9YGsiWlYBkHfXws88+m/n89LxcZ5IscSgAmydJAwDAAfvss88yO+YvLi7WVJr1yBrQzcpKnaesjqDhcJh7Z03WKkvko4hO7kOvu7vu9PQ02u32wveY8H2Xc8m5tElZQYgGlddn0XEo6pzd1GA2cNgMnAMAbD997KtZps9zE/fBRfTrrKO/oF6vL0zSMB6Po9/v36ssWckdPv/885W2dyjHXpuwXQ69/199JC+9Xi+Oj48z28x2u730ZMNDPz/3UVH3Ptuyov2yDuWeh/XrdDpxcnJy7ziSWq0WtVot51Jtj7zG8vft+vTixYulV/CeJmuY7rtcLsdnn30W5XLZNbAgRVw7XYcOm1gamG0d/SOLrpX9fn+rFscUhwJQvA82XQAAADZnmU6xfZs0nPV51h28krW/vDusrXpSvKI61w697u66ZYJFsrKbHxrn0vecS5tziPdJEdnZxjdhUXDMJoIAq9VqIfsE9p/fYwAAu8893Wq2tW8r737YdQXdpmma2declWxhlvF4nDmRZ5mVem86lGOvTdguh97/rz6Sp6xJqxHLXT+mDv383DdF3fuMx+PMa/WTJ08K2fd9bWtdEXewH96+fXuvsdtyuXyv3wW7JK+x/H27Pk2TLaxqOBzG+fl5VKvVePToUTx69Ciq1Wq0223tSU6Kuna6Dq2PWJrviaVhGyy6Xq7r2r2oD2aZ39N50h8EsHmSNAAAHLBlM8zv0wTEd+/eLXx93Z2pWfvLuyN9GzuL901RWYgPve7uujRNlzpGFxcXxRdmRziXvudc2pxl7pOy6hP5mDfIu2zbeh+bGMwGAABg++ljX83vfve7TRdhrjwnDKxzddWsZAnLTpa96c2bNwtfXyY5xPsO5dhrE7bLoff/q4/kKU3TODs7y3zfspNPDv383DebHMfdtrbuUO552IwkSeLt27cr/U2aptHpdAoq0fbIayx/H69PvV7vwRNUp4mY6vV6PHr0KE5OTu71W5M/Kera6Tp02MTScKgWJSVYV5KGRefYomQmAOwnSRoAAA7cMoMWr1+/XkNJ1iNrsGLdGSWzOoRM/tw9RXXyqbu77/PPP898z76vZrAK59L3nEublTVouY+Dytv2mUaj0dwyFTnAO2/ALEmSta+YAuwP13UAAA7NoUxwXGdfQa1Wy3zPqpNnsiZxZSWGmOVQjj3bRf8/5KvZbGaOkyw7+cT5uV8OZRx3Ge55KFqpVFop6UKv19u6ZCZFyWMsf9vanbyuT3nXg+FwGNVqNY6Pj008vadNXTvJj1ia74mlYRtsw73OonuEdbfNfr8CbJ4kDQAAB+7Zs2eZ79mnTMhZnaXr7rzJGszZts5dshU1QKfu7r5lsqIX1d52Op2YTCYrPTadMMK59D3n0mZlHR+DHMVbFORR1GoT/X5/7rm3TMIdgPvahmACAADI06H0ba174k6lUln4+ip9u+PxOHOSzTKJIWZtF9ZN/z/kL6tPfNHksJucn/vlUMZxl6GusA6VSiWazWbm+zqdzkFNkM1jLH/b2p28rk9pmsbXX3+91OJdqxiNRnF6ehqNRiPX7R6CTV072V9iaThkx8fHc1/bxt8Mm+Y7ASieJA0AAAdumcCq0WgUw+FwDaXhfZeXl5suAis6pAHPRdTdu7ICZ6fa7XbBJdkNzqXvOZc2K6seGvAuXq/Xm/ta3kElU4tWo6lWq4XsEzgMrhsAAByaRZNikiRZObFsno88g1PX3ZdZr9cXvr4oaP59Wf3R5XL5Xt/VoRx7eAj9/+yCrGtORMTFxcUaSrJezs/Firr3yZpQve7EWMtwz8O6ZMX3JUlS6Mrp2+gQx/JXuT4lSRKDwSDOzs5yL8f5+Xlhk8D31Sauna5D+00sDYdsG38XvG+d9yH7eM8DsGskaQAAOHBJkizVKbfpFdXXZd2dFVmDzDqrd8+mjpm6uxuWSdSwaEDjkDiXvudc2qx9PD5ZZV5mRZF1mpf9P0mSwgIZ3rx5M/e1QwtuAtZrF4IJAABgFYt+u+9T8Oi6+4iWSZywbDLg169fL3x9mcm5sxzKsWe36P+H1S0TS5LHueX83C2b+n62bQwrwj0P6/P06dOFr4/H44ObILuOtnwfrk/NZjOurq6WWshrFf1+P05OTnLd5j4r6trpOpQfsTTZxNKwLYpKRJKn0Wi06SJcE4cCUDxJGgAAWCrA6lBWdl93Z2pWZ/iPf/zj9RSEnafu7oZl2tt+v79VnbSHxrnETVnHxyBGsYbD4dxjUNQA76KVJpdJtAMAAAD8SVbfiQkD95c1wSYr+ULE98HCw+Fw4Xvu2x/i2LON9P/D/WRN8spjXNP5SUR2XdvG+wf3PKxDtVrNvG+P+H6c875J1nbROsby9+X6lCRJtFqtuLq6ilarldvk1uFweFB1bhu5Dh0msTQcuqISkQDAfUnSAADA0pmS9yFRw7YN6mYFLOhMYkrd3Q/LrHAW8X0me4rhXGIV+7iS0i4N0s/L/B8RcXp6Wsg+O53O3NcEmABF28XrCgAALHJ8fLzwdclq7y+rn2I4HGZ+v91ud+HrD1lp1bFnE/T/QzGy6urvfve7B2/D+UlE9hjWtq1gHeGeh+Kdn59n3rff1G639yK+bxl5jOUf2vUpSZKo1WoxGAzi6uoqOp1O1Gq1ByVtaLfbK9VR8uU6lB+xNIuJpWHbzLvOb8u5uk2xH9tUFoB9JUkDAAARsVygVaPRWENJipXV2bDujul9nPxJMdTd/fHixYvM97TbbQNlBXEusYp1rL6xbrtUp3q93tzXisr+/+bNm7XvE2BqF68rAACwyLb1xe2TNE0zJ9K0Wq0Hvf6QIHvHnk3Ytnqn/599kVVXsyYHLrMN5ycR2zdZehnbVrfZL/1+/16xevV6feEE3n2Rx1j+tp3D67w+JUkSlUolWq1WDAaDmEwmMRgMotlsrjwuvg8xpbtq2+rwLtul+z+xNBDx5MmTmc9vS2K3bUr8Jw4FoHiSNAAAEBHLdZaPx+Od71TP6vhY1IGZt9FolDlgtE0dNWyWurs/zs7OlhrYkWW6GM4lljUcDjPfs4vHZ5dWIZoXPJQkSSHf/XA4nHtOViqV3Pe3Se12O46Pj+PRo0dxdHR0kI/j4+M4PT3dyqBO9tMydW2Xgn8AAGAZ29QXt4+y+pAXrWY6Go0WTtZYJgnEIo49m7BN9U7/P/ska3LfMhM+nJ8sY5n+0WXG79Zpm+o2+2U0GkW1Wp37erlcXni/Xq1W93pydl5j+dt0Dm/D9alUKsXZ2Vn0er24vLyMZrO5VNs8Go0OIjHINtqmOrzrxNLMJ5bmsB67Ektzeno68/l9vv+ZRRwKwHaQpAEAgIj4vsO2Vqtlvu/8/HzrO18W+eyzzxa+fnFxsaaSzO8snSqq05TdpO7ulxcvXmS+p9/vG8QsgHOJZb1+/Xrh60mS7OQgRlaZLy8v11OQDIsGzYrKwr9o9ch9S5xTr9eXCvTZZ9NgoUUrPkCeDi0YAAAAIrJ/w/tN9jCff/75wtcXJWJYlMAh4uF9IY49m6D/H4qRx4RR5yfLykoStW3j5+55KMqiyZFJkkSn04lOpzN37Hc8Hs+dvLgP8hrLd32aL03TODs7i6urqzg7O8t8f7PZXEOpeJ/rUH7E0swnluaw7EoszbzfDes6dosSt6wznlAcCsB2kKQBAIBry3aWP3/+vOCSFCcrc+t4PF5bp0VWpuKiOk3ZTerufjk7O1tq8HTfBjK2gXMpP9uUJb4IWUEQT548WVNJ8pXV9mzLQOOi77+ogKJ5A4xJkqx0PjYaja0LEnyfANM/WWaVN8hD1nVzFxP/AADAMhb9pl5nX9w+WqbPYl4yhkUB9hH5rITo2LNu+v+hGFnnzTL9Ws7P/Oz7+NyzZ88Wvr6NK4K75yFv1Wp1Yb2ZJmdI0zRevXo1932j0WhvEzXkNZbv+rScZrOZGVe6zePj+37tdB3Kh1ia+cTSHKZtj6VZVPfW0e4tahPWWY/EoQBshx9sugAAAGyPJEni7Owszs/PF76v2+1Gv9/f+sHpWdI0jTRNF3bCtFqttWR3zupwzBp85rCou/un1WplDo6MRqNoNBoyzufIuZSfbRmALMJoNIrhcLjwPbsa0PLjH/944evbMkC/KAiliHvQfr8/t05nrUp50/n5eZyfn0e/34/BYJBT6fLX6/Wi2WzGu3fvtuaYr9Pjx48jTdM4OTnJZdIJLCPrXBPwAQDAvjo9PV3YD7auvrh9Va/XF36/r1+/vrPq6Wg0WvgbpVQq5fIbxbFn3fT/Q/7yWtXb+ZmffR6fi8geA5qO52zTZCP3POTp/Px8bqK1iIharXbrPKlUKlGr1aLdbs98f7/f37uYkzzH8l2flnd2dhatVmvhdzUcDueubL5J+37tdB3Kh1ia2cTSHJZdi6UplUoz7wlGo1Hh8ReLkiOs85otDgVgO0jSAADALc1mM/r9/lKDGZeXlzv5A75cLs8dnIqIaLfbhXdMt9vtzAGAXUyCQbHU3f1SLpcXDpZPnZ+fx/HxcdRqtTWVbP85l/Kxz6sNLHP8t/34zJN177Ytx3VeEMGyAZ+rWrR6ZL1eX3o7r1+/jojtH+RK0zRzxUwgX1nX/W1fiQIAAO6rUqlEo9GY+/o6+uL2WaVSiSRJ5v7mGA6HdyYyLpr0FbFaX0hW2Rx71k3/P+Sr0+ksfH2ViVnOz3xsyzhOUUql0sJ7m4jvj+P7Sag2yT0PeZkmVJhn3vheq9WKi4uLubF++xZzkvdYvuvT8hqNxsLfi6PRaCuTNOz7tdN1KB9iaWYTS8M2e/bs2cz7n+FwWPh1c9F1e53XbHEoANvhg00XAACA7ZM10D51enq6k5mGq9XqwtfH43FmgNpDZXV8Twee4SZ1d/+0Wq2lvq+s1dBYjXMpH1kJnXbVeDzOTJ6SJMlWBhcsI2vAcxvu7Uaj0dxyFDGQteicT9N06WM9Ho+vz4tNr2QCbJ/f/e53C1/f9oAUAAC4r6zf1uPxOM7Pz9dYov2TNUH2/b7laWD8PHlN3nLs2QT9/+y68Xi8NXEYy4yXLLtSeYTzMy/7Oj53U9a9zbZNnHPPQx5Go1Fmm7po9fSsWL96vb4X7UcRY/muT8t78uTJwte3ZRL7+/ah7i/iOpQPsTR3iaVh21UqlZnPf/XVV4XvezQazXx+3TGF4lAAtoMkDQAA3LFsNtDRaJQ5ULGNyuVyZifI8+fPC9t/u92e20EzJXvxarahE3wd1N39tGxinGq1mvn9sxznUj6ygrnXoYj2f5ljv8urjGQNvmxDO7MoSGKVgM9lvXnzZu5r8wb0srazDSuZANslq309OTlZU0kAAOCuovvYs/q6Xr58eTD9/EXIWrnwZj/ezcD4WVbpC1mGY7+bdvmY6P9n111cXES/34+Tk5ON99cvWpF5apXrhvMzH9swPle0rOMwGo22brKpex4eKmsMtNVqLRznTdM0M/bk6dOna7227MpYvuvT8rISRWzrat2una5DyxBLc5dYGrZdmqYzz911LIY2LxHEuuu4OBSA7SBJAwAAM9VqtaU60vr9/sYTNYzH45XLkNUxPR6PM4Pa7mM0GmVut1Qq6Yx8T9Ygz7Zm4i6Curt/yuXyUu3teDzeioCsfXGo51Jeg65Zwdzrknfm63a7vdQqGEXUjXVZZoWNTQ/OLwqSKOLcWJScbJVjPd1OpVLZipVMgO2SdQ9nBQMAAIq06T72rEkv9xnnua9N93sUoVQqLfxNcTMwOStIOe9+L8d+O226TSjaofb/sx+mfUij0SiOj48LX7l7nuFwmLlS+X0SWh/q+blv43NFS5Ikc/y80Wis7dq+zH7c8/AQp6enC8cQyuXyUm1upVJZ+L7xeFzIJN55dmks/1CvT6vKGuvKc4zctXM1rkMPJ5bmLrE07IJZyQXH43HhMbbzri3rjikUhwKwHSRpAABgrlevXi3VKdbtdtc6iHNTu92OR48eRbfbXakTdJks2MsO7qximc7uV69e5brPfZCVaXsdmU+3hbq7nzqdzlIdouPxOI6Pjw+qzhflUM+lvIJrl1k9aR3Oz89zGwQdDodLDZRUKpWdH8DY9hUA5rVxSZIU8t3PGzjLmuBw02g0ut7OLifxAIpjcBwAgE3ahj72Fy9eZJah6BWR+/1+fPLJJ4XuY1MW9UfcnJSyKKA/SZJCgvod++2zDW1CkQ61/5/9MBgMbv27Wq1GtVpd64Sw8XgcT58+zXzffVb1PtTzc9/G59Zhmfq1TD19iOmE9pcvXy71fvc83Eej0Vh475UkSXQ6naW312q1Fo43jEajtcX47dJY/r5cn4q+Z8hKdpDnWJdr5+pchx5OLM1tYmnYBfMSVBWZ8HBeEog0TdeeWEkcCsB2kKQBAIC5kiSJt2/fLvXefr8fx8fHa8s8PBwO4+Tk5FbH3aqd88sMYlSr1dw6a05PTzO/n2UGfQ5RVrKQ94NVlrHpzMYPoe7up8FgsHS26NPT04MaSCzKIZ5LebR9/X4/c/WkdXr+/PmDtzG9r1hG1sD2LsgagLm4uFhTSe4ajUZz6+mTJ09y39+ic/LZs2dLb2caLLiJATdg+43H48xrsMFxAACKtA197JVKJfM3c6PRKKzfqdFoxOnp6VL357soa2XdaSD/oglg91kRfRmO/fbZhjahaIfY/89+mNU/3+1249GjR2sZm5kmaMg6p2u12r1XwT3E83Mfx+eKlqZp5r3JcDgsbLL5dMGYfr+/9IRM9zysqtvtZk6Y7nQ6K7e3vV5v4ev9fn9t8Sa7NJa/69en0Wh0fc9Q1ET8RUlr8p4k7tq5OtehhxNL8ydiadgls343tFqtwvY373p4n0SGDyEOBWB7SNIAAMBCpVIpc/BmajQaxcnJSaEDOaPRKKrVapycnNzpCFy1c7dUKi3VKVKtVh/UOT0ej+Pk5CRz1ZlVs58fkqwVfd68ebPS9obDYXzyySdrSyqSN3V3PyVJslKihvPz8zg+Pi406+++28dzKav+fPXVVw/afpHBTvfV7XaXWmFi0d8vG9RRqVT2Iog26zNcXl6uqSR3LTpPiqh7iwLrKpXKUtsYj8fXbYQEOsAsVi8AAGDTtqWPfZkJRvV6Pdff19MJdjcnqeS1Iuc2SZJkYbB7r9dbGNAfUeyKho79dtmWNqFI+9j/v4+mq2IvemxTvVqHRZ+3Xq8XmqxhGu+xzHf+kMkn+3h+HuL43Dq0Wq3MvtN+v389ITQP03HDm/dFq2zbPQ/Lmsa/LXJ2dnavCa1pmma2a+fn52uJNdmlsfxdvz7dvH43Go3c44nq9frC9nDVuuraWQzXoYcRS/MnYmnYJbOu36PRKPNae1+z7gPSNF363MiLOBSA7SFJAwAAmcrl8tKJGiK+H8iZdrzmNRDa7/ejWq0uHEBYNnv9TcsOaNXr9aUDEm6afhfL/N3bt2/vvdrEvjs+Pl74+ng8XqojdzweXx/L8XhcWCfcOqi7+ylN05USNUwH7o+Pj6Pdbj+ozR2Px9Htdq/rTJEBudtk386lrOzoD6kn7XZ76QCIdet2uysHYI3H46hWq0sHhSRJstTqGbvgs88+W/j6JoNeF91zFpFVf15wwCorfUwzpCdJUtiKk/AQuxoEc9Ouf4asVVX2IQEQAADbbVv62JedZHJ+fn6vvrib5Ww0GvHo0aOZE0l2dUXHLIv6dC8uLhZ+n6VSqdDAXcd+u2xLm1C0fev/30fv3r2Ldru98LHr/UKrWCbeYXrePXr0KBqNxr1iJGaZJodfZnv3WdX9fft2fh7q+Nw6LBOr1O/345NPPrn3pOnxeHxdZ6rV6p16s8p55p5nPUajUQyHw+j3+7ce3W4390fWdermY1nj8ThzQu2yCQPmqVQqmeOWs+p7EXZpLH+Xr0/vJzWYxhNN24n73jNMj0VWHV81xsi1sxiuQw8jluZPxNKwS5IkmXnf1Gq1ct9Xt9ud2cYVsa8s4lAAtsgEAACWNBgMJkmSTCJipUe5XJ50Op3J5eXlSvtqNpuTSqWy9D5brda9P1uapit9nlarNbm6uppb9rOzs5W+q4eU/aas/axyDLJcXV1l7m/ed7SqwWCw1PdYq9Vm7rPT6Uwqlcqd95dKpZXLknVc1+0Q6m6SJLnsY5dcXl6udGzfP87NZnPS6/Uml5eXt4731dXV5PLycjIYDCadTmfldvah589NzqVizqWzs7PMfaVputL1oNPpTEql0r3q40OvA6vWzSRJJs1mc+F+B4PBpFarrfxZOp3Ogz7LNrm8vMysI5uy6JgXodPpPKiNu/ld5nUeQ96azWYh1/J12vXPkHV9bjabmy4iAMBWW+Z3vD72xbapj30yWa4Pa/qoVCpL9ctMx5XK5XLu9WXb+jIXyfouiz6HsuzasdcmFN8mLNp/XuND+9L/v4/1sdfrZe6n1+s9/APtiHn91VmPUqk0aTabk8FgsNL+rq6uJs1mc6Vz5OzsLNfPvC/n56GNz61bq9Va+rMnSTI5Oztb+F1fXV1Ner3e5OzsbKk6eJ/r0a7d82yTVb67bXsse44vcwzzuq5n1fEkSe7dZuzzWP4uXp+WqVdpmk5qtdqk1Wpl1rFVyn2f+ALXzmK5Dt2PWJo/EUvDLpp1/c6zT+Hq6mrmubipeA9xKADb42gymUwCAACWNBqN4vT09N7ZlZMkicePH0eaptfZnqdZJd+9exej0ejemXTPzs7unUX8IZ9r+lnuW/ZmsxlnZ2cr/90sR0dHC1+/vLzMbSWk8Xgcjx49Wvieq6ur3FaFyfpsN00/47t37zKPyao/iR49erRwm+v+iXUIdTdJkri6usplP7tkmo1+21aemiqVSjEYDO79986lP8nzXOr3+5mrbkyVSqUol8txfHx869owHo9jNBrFV199Ff1+/0EZ7h96HciqJ4ukaRqlUikeP358fY9x32P0kHuMbZV1Xd1El+FoNJq7il+5XF5qtaS89lmpVJZaYWK6ukRR5YM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sZm4/IuLw8HDgc7VabeBzd+7cGfjcwcFBlMvlkep42M7OTt/HG43G0N9Lta5J7O/vD9zuqXq9PnR/XeXhz1vW5wCWiXxyn3xymXzSn3wCjKro51qma2trK7rdbhwcHMy7lLla9EyXam5Kta5JyHMAwFWKPkZb9OwckW5OTbWuScjPAGmQT+6TTy6TT/qTT4BRFP08y3S5Vnvfoue5VDNTqnVNQpYDYKnMe5UIAFg2/VYTrFQq8y5rJP1W+zz9qlarmdvpt0Li6e83Go2RV1x82LCVG6+qc9BqoKVSKfP2r9Kv/atWYU21rlOjrpY5rC+dfuW1UvEodQEsG/nkAflEPpFPgGlYhHMt03N0dJTL8X8R/nWWRc90qeamVOs6Jc8BAHlbhDHaomfnXi/dnJpqXafkZ4Bikk8ekE/kE/kEyNsinGeZHtdqH1j0PJdqZkq1rlOyHAAMdz0AgJlaX1+/8H25XI7Pf/7zc6pmPMNWLRxlJcuXX3750mOn78+tW7cmbn/Yz25sbAx8rtVqXVrB89Tm5mbm7V+l34qj/VYPTb2ucTWbzaH7ISJid3c3t5WKS6VS38f39/dHXoEVYNHIJw/IJ/KJfAJMwyKca5mejY2NKJVKsbu7O+9S5m6RM12quSnVusYlzwEAWSzCGG2Rs3NEujk11brGJT8DpEM+eUA+kU/kEyBvi3CeZXpcq31gkfNcqpkp1brGJcsBsIws0gAAM7SxsRGdTufs+0qlEkdHRwMHiCnrN1g/lWVQ2+12o9VqXXq8UqlExP1Bc7lcvvR8p9O58B4Oc3BwMPC527dvD3xuWP1bW1uZtp3FwxO/p+7du9f38VTrGkez2Ry4nVPb29u5TcJEDJ7Yi7j/mg8PD3PbFkCRyCcPyCfyiXwCTMMinWvJ397eXnQ6nXjppZf0if9uUTNdqrkp1brGIc8BAFks0hhtUbNzRLo5NdW6xiE/A6RDPnlAPpFP5BMgb4t0niV/rtVetqh5LtXMlGpd45DlAFhWN+ZdAAAsi729vQsDvUqlEp///OcLO6mzsbExcGLg4OBg6CRNxOBJhfO/V61WY39//9LPHB4eZhqgD9pGpVIZ+r43Go2Bz62url653UkNmmRKta5RtVqtKydhNjc3c1+Vdmtr68rVU8vlcuzs7ES1Wu07CQiwaOSTi+STweQT+QQYT5HPtc1mMxqNRnS73Tg+Pp53ORNbWVmJ1dXVqFarZ38AMm+dTufsGD/sXyVZNoua6VLNTanWNSp5DgDIoshjtH4WNTtHpJtTU61rVPIzQDrkk4vkk8HkE/kEGF2Rz7Ou1U6fa7X9LWqeSzUzpVrXqGQ5AJaZRRoAYAaazWbs7OycfX+6EmuR3b59e+AKjVlWynz55ZcvPfbwxNv6+nrfSZhGo3HlJMywlQ+ff/75ob+bpf5parfbfR9Pta5RdDqdePbZZ6/8uWlMgtdqtSiXy0MnlDqdzlm/LpVKcevWrahUKrG+vh63bt0qxOQ8QFbyyWXyyWDyiXwCjK7I59osF9CLrN1uJ3Hx/fT4Xq/X51xJWhY106Wam1KtaxTyHACQRZHHaIMsanaOSDenplrXKORngHTIJ5fJJ4PJJ/IJMJoin2ddq50N12r7W9Q8l2pmSrWuUchyACy76/MuAAAWXafTuTBZVqSJvmFKpdLA1Uw7nc6VKyv2m1R4eHXNQattZpmQGLay5FWreM5bt9uddwl9TVpXp9OJtbW1TO3s7e1Fq9WaaHv9jDKZ2u12o9lsxt7eXqyvr8fNmzdjdXU1dnZ2plIbwCzJJ/3JJ4PJJ/IJMJqin2vz+tcSUpXCee3w8DCazWbs7u4m8UdIKZHppiOFft+PPCfPAcAsFH2MNojsPD3ys/wMMG3ySX/yyWDyiXwCZFf086xrtdPnWu1g8tx0pNDv+5HlZDkAJmeRBgCYom63G2tra2ffV6vVQk30XWXcFSdbrVbfwfjDK78Om+gZthJmRMTdu3f7Pj6szYg0JkGOj48vPZZqXaNoNpsjvY6NjY2JttdPtVqdaNXbTqcTe3t7sba2Fmtra3NfwRRgHPJJf/LJcPLJffIJkMUinGtrtdrZvziwSMrlcmxubg49785Ct9uNF154Icrl8pX/EsiyWrRMl2puSrWuUchzAMBVFmGMNsyiZeeIdHNqqnWNQn4GSIN80p98Mpx8cp98AlxlEc6zrtVOl2u1V1u0PJdqZkq1rlHIcgBgkQYAmJrTib7TgWe1Wh26emMRDVtx8uDgYOBzgwav/dobtI1h7+WgSZ6IiNu3bw/8vYh0V6BNta5p6nQ6sbOzk3u7m5ubQ/tnVq1WK9bX12NrayuHqgBmQz6RT/KUal3TJJ8AV1mkc+3BwUG02+3o9XoL89Vutye6OJ+XnZ2d6Ha7uRz7F9WiZbpUc1OqdU2TPAcAy2WRxmiDLFp2jkg3p6Za1zTJzwD5k0/kkzylWtc0ySfAMIt0nnWtdnpcq73aouW5VDNTqnVNkywHwCKySAMATMmzzz57Nniu1WqFnegbplKpRKlU6vvcsFUEX3755UuPDZpsGbQa56CVMK/a9iQrMDYajZlMRI46SZBqXXnY29uLVquVe7u1Wi3a7XYuK/Lu7+/H6upqEiuaAlxFPpFP5JPJySfAMMtwrmUyrVYr9vf3k/hXYlK2TJku1dyUal15kOcAYHkswxhtmbJzRLo5NdW68iA/A+RLPpFP5JPJySfAIMtwnmUyrtVms0x5LtXMlGpdeZDlAFg0FmkAgCnY2Ng4GzzWarWFXm1z2MqT/QbQ3W637+Pr6+t92xg0UO52uwNXkOw3yXNq2OqeETFwUul0m/OSal2TKpVK0W63h76+SS6EDlMul+Po6CgajcbEEzKdTufCCswAKZJP7pNP8pNqXZOST4BxLdO5lvFtbGxEqVRK4l+JSd0iZbpUc1OqdU1KngMAIpZrjLZI2Tki3Zyaal2Tkp8BZkc+uU8+yU+qdU1KPgHGsUznWcbnWm12i5TnUs1MqdY1KVkOgGVjkQYAyNnOzk4cHh5GxHJM9A0bJPebDBm0imWtVhvYzqDnTt/nhw1aXXHYNk6trKwMfG7QpM8spFrXJEqlUhwdHUW5XB76Oel0OrGzszO1OqrVahwdHUW73Y7d3d2xJ2U6nc7UJo0AJiWfPCCf5CfVuiYhnwDjWrZzLePpdDrR6XSi2+3GtWvXcv/qd8w/PDzM9LspWqRMl2puSrWuSchzAEDE8o3RFik7R6SbU1OtaxLyM8DsyCcPyCf5SbWuScgnwDiW7TzLeFyrHc0i5blUM1OqdU1ClgNgGVmkAQBytLe3F3t7exGxPBN9w1ae7Dfh0mg0Lj1WKpWiXC4PbGfQKpr92ho0yRMR8fzzzw987nwtg7Tb7St/f1pSrWtc5ydhIu73o83NzYE/v7e3N3ByLS/lcjm2t7fj6Ogoer1eHB0dRb1ej1qtNvT9P6/ZbA6cHASYF/nkIvkkP6nWNS75BBjXMp5rYRYWKdOlmptSrWtc8hwAELGcY7RFys6ntQwiP+dHfgaYHfnkIvkkP6nWNS75BBjHMp5nYRYWKc+lmplSrWtcshwAy+par9frzbsIAFgEh4eHZyvlbW5uRr1en3NFs7OxsTFw8Plw1FhdXb20uuNV71e3242bN29man9rayv29/f7/uzJyUmmAXW/GiMiKpVKHB0dXfn705JqXed1Op1YXV298ueOjo76rkp58+bN6Ha7fX+nXC7PddKp1WpFvV6Pu3fvDqwxIq39ASCfyCfTlmpd58knae0PWDTLfK5ldN1ud+gfj0zqzp07l/6IoVKpxIsvvnjl72b51+LmYZEyXaq5KdW6zpPn0tofAJCyZR6jLVJ2HlRjxPxzUap1nSc/p7U/AOQT+WTaUq3rPPkkrf0Bi2SZz7OMzrXa0S1Snks1M6Va13myXFr7A4AE9QCAiTUajV5E9CKit7m5mWvbJycnubY3DQcHB2ev/+GvRqNx9nMnJyd9f+bg4ODKbZTL5SvbH/ZzlUol8+vZ3Nwc+HrmuT9Sreu8drs9sMbTr6Ojo4G/f/6z1O9re3t7hq9msGH74qrXCDAr8ol8Mgup1nWefCKfwLQs+7mW9NRqtUvH/1qtNu+yJrJImS7V3JRqXefJc/IcAGSx7GO0RcrOvV66OTXVus6Tn+VnIB3yiXwyC6nWdZ58Ip/ANCz7eZb0uFabdp5LNTOlWtd5spwsB8Bw1wMAmEin04n19fWIyH8l1o2NjYErRKakWq0OfO78qqCDViDNsgLooJ9pNBpn/93tdvuuJhkR8fzzz1+5jVOn+7Ofaa6iepVU6xpFo9Hou0rmqWq1GpubmwOf39vbu7TS7DzU6/WhdRZlfwCLSz6RT2Yl1bpGIZ8A43CuhdlYpEyXam5Kta5RyHMAgDHaYmXniHRzaqp1jUJ+BpgN+UQ+mZVU6xqFfAKMynkWZmOR8lyqmSnVukYhywGw7CzSAAAT6Ha7sba2FhH5T/RF3J/AGDZoTUWpVBpY5/lJkvP/fWrYBM55gyZRzg94hw1+s0z0ZPnZO3fuZG4nb6nWldX29nam/V2v16NcLg98fmNjI8+yxlav16NUKvV97pVXXpltMQDnyCf3ySezkWpdWcknwDica2F2FinTpZqbUq0rK3kOADBGu2+RsvNVPys/j09+BpgN+eQ++WQ2Uq0rK/kEGJXzLMzOIuW5VDNTqnVlJcsBgEUaAGBspxN93W53KhN9EfdXex02IE3JuJMkw1aAPG/QJM/5lRP7TfJERJTL5ZHfx0ETBq1Wa66rNaZaVxZPPPFE5p8d9nnqdDqxs7OTR0kTu3XrVt/HB63YCjBt8slF8slspFpXFvIJMCrnWpi9Rcp0qeamVOvKQp4DgOVmjHbRImXniHRzaqp1ZSE/A0yffHKRfDIbqdaVhXwCjMJ5FmZvkfJcqpkp1bqykOUAwCINADC2Z599NjqdzlQn+iIinnnmmdzbnoZhKzmeThD0G5zmsSL5afu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",
      "text/plain": [
       "<Figure size 4800x2400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "gs = fig.add_gridspec(40, 8)\n",
    "\n",
    "locs = [[fig.add_subplot(gs[:14, :4]), fig.add_subplot(gs[22:, :4])], [fig.add_subplot(gs[:14, 4:]), fig.add_subplot(gs[22:, 4:])]]\n",
    "\n",
    "for i, g in enumerate(_GROUPS):\n",
    "    ax = locs[i // 2][i % 2]\n",
    "    sns.swarmplot(ax=ax, data=stacked_data[stacked_data.group == g], x=\"level_1\", y=\"freq\", color=tab10[i])\n",
    "    avg = stacked_data[stacked_data.group == g].groupby(\"level_1\")[\"freq\"].mean()\n",
    "    for j, t in enumerate(_TIMES):\n",
    "        ax.eventplot([avg[t]], orientation='vertical', lineoffsets=j, linelengths=0.5, color=\"k\", ls='--', lw=3)\n",
    "    \n",
    "    ax.set_title(r\"{} ($n={}$)\".format(_LABELS[g], data[data.group == g].shape[0]), fontsize=26, y=1.01)\n",
    "    \n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.set_xticks([j for j, t in enumerate(_TIMES)])\n",
    "    ax.set_xticklabels([_LABELS[t] for t in _TIMES])\n",
    "    ax.xaxis.set_minor_locator(mticker.NullLocator())\n",
    "        \n",
    "    ax.set_ylim(bottom=0, top=30)\n",
    "    ax.yaxis.set_major_locator(mticker.MultipleLocator(5))\n",
    "    ax.yaxis.set_minor_locator(mticker.MultipleLocator())\n",
    "    ax.set_ylabel(\"Consumption Rate\")\n",
    "    \n",
    "    if i // 2 == 1:\n",
    "        ax.yaxis.tick_right()\n",
    "        ax.yaxis.set_label_position(\"right\")\n",
    "\n",
    "fig.savefig(\"Figure2.png\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b3221344",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 2.657185\n",
      "         Iterations: 18\n",
      "         Function evaluations: 22\n",
      "         Gradient evaluations: 22\n",
      "                     NegativeBinomial Regression Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:              Post2Freq   No. Observations:                   35\n",
      "Model:               NegativeBinomial   Df Residuals:                       31\n",
      "Method:                           MLE   Df Model:                            3\n",
      "Date:                Wed, 07 Feb 2024   Pseudo R-squ.:                  0.1094\n",
      "Time:                        16:01:26   Log-Likelihood:                -93.001\n",
      "converged:                       True   LL-Null:                       -104.43\n",
      "Covariance Type:            nonrobust   LLR p-value:                 4.321e-05\n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept                0.6803      1.077      0.632      0.528      -1.431       2.791\n",
      "C(Treatment)[T.True]    -0.6295      0.304     -2.072      0.038      -1.225      -0.034\n",
      "np.log(Size)             0.0721      0.121      0.596      0.551      -0.165       0.309\n",
      "PreFreq                  0.1308      0.025      5.256      0.000       0.082       0.180\n",
      "alpha                    0.4887      0.203      2.408      0.016       0.091       0.886\n",
      "========================================================================================\n"
     ]
    }
   ],
   "source": [
    "mod1 = sm.NegativeBinomial.from_formula(\"Post2Freq ~ C(Treatment)+np.log(Size)+PreFreq\", data=data_alt).fit()\n",
    "print(mod1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c6763ced",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 2.108166\n",
      "         Iterations: 21\n",
      "         Function evaluations: 25\n",
      "         Gradient evaluations: 25\n",
      "                     NegativeBinomial Regression Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:              Post4Freq   No. Observations:                   35\n",
      "Model:               NegativeBinomial   Df Residuals:                       31\n",
      "Method:                           MLE   Df Model:                            3\n",
      "Date:                Wed, 07 Feb 2024   Pseudo R-squ.:                  0.1220\n",
      "Time:                        16:01:26   Log-Likelihood:                -73.786\n",
      "converged:                       True   LL-Null:                       -84.039\n",
      "Covariance Type:            nonrobust   LLR p-value:                 0.0001332\n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "Intercept               -2.7562      1.574     -1.751      0.080      -5.842       0.330\n",
      "C(Treatment)[T.True]    -0.4642      0.391     -1.188      0.235      -1.230       0.302\n",
      "np.log(Size)             0.3797      0.175      2.164      0.030       0.036       0.724\n",
      "PreFreq                  0.1593      0.032      5.002      0.000       0.097       0.222\n",
      "alpha                    0.7305      0.344      2.124      0.034       0.056       1.405\n",
      "========================================================================================\n"
     ]
    }
   ],
   "source": [
    "mod1 = sm.NegativeBinomial.from_formula(\"Post4Freq ~ C(Treatment)+np.log(Size)+PreFreq\", data=data_alt).fit()\n",
    "print(mod1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8c8da9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 2.644369\n",
      "         Iterations: 24\n",
      "         Function evaluations: 28\n",
      "         Gradient evaluations: 28\n",
      "                     NegativeBinomial Regression Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:              Post2Freq   No. Observations:                   35\n",
      "Model:               NegativeBinomial   Df Residuals:                       29\n",
      "Method:                           MLE   Df Model:                            5\n",
      "Date:                Wed, 07 Feb 2024   Pseudo R-squ.:                  0.1137\n",
      "Time:                        16:01:26   Log-Likelihood:                -92.553\n",
      "converged:                       True   LL-Null:                       -104.43\n",
      "Covariance Type:            nonrobust   LLR p-value:                 0.0002419\n",
      "=========================================================================================\n",
      "                            coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "Intercept                 0.8173      1.079      0.757      0.449      -1.298       2.933\n",
      "C(group)[T.Debunking]    -0.6327      0.387     -1.634      0.102      -1.392       0.126\n",
      "C(group)[T.Reject]       -0.8355      0.398     -2.097      0.036      -1.616      -0.055\n",
      "C(group)[T.Source]       -0.3952      0.414     -0.955      0.340      -1.206       0.416\n",
      "np.log(Size)              0.0599      0.121      0.496      0.620      -0.177       0.296\n",
      "PreFreq                   0.1253      0.026      4.741      0.000       0.074       0.177\n",
      "alpha                     0.4723      0.199      2.374      0.018       0.082       0.862\n",
      "=========================================================================================\n"
     ]
    }
   ],
   "source": [
    "mod2 = sm.NegativeBinomial.from_formula(\"Post2Freq ~ C(group)+np.log(Size)+PreFreq\", data=data).fit()\n",
    "print(mod2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "515b96ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 2.053732\n",
      "         Iterations: 28\n",
      "         Function evaluations: 31\n",
      "         Gradient evaluations: 31\n",
      "                     Results: NegativeBinomial\n",
      "====================================================================\n",
      "Model:               NegativeBinomial  Pseudo R-squared:  0.145     \n",
      "Dependent Variable:  Post4Freq         AIC:               157.7612  \n",
      "Date:                2024-02-07 16:01  BIC:               168.6487  \n",
      "No. Observations:    35                Log-Likelihood:    -71.881   \n",
      "Df Model:            5                 LL-Null:           -84.039   \n",
      "Df Residuals:        29                LLR p-value:       0.00018863\n",
      "Converged:           1.0000            Scale:             1.0000    \n",
      "Method:              MLE                                            \n",
      "--------------------------------------------------------------------\n",
      "                       Coef.  Std.Err.    z    P>|z|   [0.025 0.975]\n",
      "--------------------------------------------------------------------\n",
      "Intercept             -2.9123   1.5927 -1.8285 0.0675 -6.0340 0.2094\n",
      "C(group)[T.Debunking] -0.8495   0.5523 -1.5383 0.1240 -1.9319 0.2329\n",
      "C(group)[T.Reject]     0.0833   0.5115  0.1629 0.8706 -0.9192 1.0859\n",
      "C(group)[T.Source]    -1.1762   0.6149 -1.9130 0.0557 -2.3813 0.0289\n",
      "np.log(Size)           0.3725   0.1742  2.1382 0.0325  0.0311 0.7139\n",
      "PreFreq                0.1934   0.0397  4.8672 0.0000  0.1155 0.2713\n",
      "alpha                  0.7140   0.3196  2.2340 0.0255  0.0876 1.3404\n",
      "====================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mod2 = sm.NegativeBinomial.from_formula(\"Post4Freq ~ C(group)+np.log(Size)+PreFreq\", data=data).fit()\n",
    "print(mod2.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0ab3dc3-af1a-482c-a3ba-e75827cd5988",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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